{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to pandas\n",
    "by Maxwell Margenot\n",
    "\n",
    "Part of the Quantopian Lecture Series:\n",
    "\n",
    "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
    "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas is a Python library that provides a collection of powerful data structures to better help you manage data. In this lecture, we will cover how to use the `Series` and `DataFrame` objects to handle data. These objects have a strong integration with NumPy, covered elsewhere in the [lecture series](http://www.quantopian.com/lectures#Introduction-to-NumPy), allowing us to easily do the necessary statistical and mathematical calculations that we need for finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With pandas, it is easy to store, visualize, and perform calculations on your data. With only a few lines of code we can modify our data and present it in an easily-understandable way. Here we simulate some returns in NumPy, put them into a pandas `DataFrame`, and perform calculations to turn them into prices and plot them, all only using a few lines of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OQUREREREjkLT3kRERERE5JSg5EdERERERE4JSn5EREREROSUoORHREREREROCUp+RERE\nRETklKDkR0RERERETglKfoJk9+7dzJ8/n2XLloU6FBEREREROQwlP0HQ2dnJPffcw8yZM0MdioiI\niIiIHIGSnyCw2+088sgjJCUlhToUEREREZEhyzAMXA378Hm7+6V/a7/0GiJPbn6B9eWbgtrn2dnT\nWDrl8qO2MZvNhIWFBfW6IiIiIiKnGmfFesp2vUh0YgEF076LyRTcsRqN/IiIiIiISMh5PZ1UFa4E\noLVhHzUla4J+jWE18rN0yuXHHKUREREREZHBp6b4HbyedlJzZ9NYs5mqwpVExY0gOmFk0K6hkR8R\nEREREQkpd4eTurIPCQuPJ2PUAvInfwtMJkq2PY3H3Ra06yj5CYItW7Zw8cUX88wzz/DII49w8cUX\n09LSEuqwRERERESGhIq9r2MYPjJHL8ZssREVN4LMUQvxuF3s3/4MhuEPynWG1bS3UJk8eTIrVqwI\ndRgiIiIiIkNOa2MRzXXbccTlEp86OfB66ojZtDYV43LupqZkDen5F5z0tTTyIyIiIiIiIWEYfir2\n9AwiZI/5OiaTKXDMZDKTN+EqbPZYqgpX0tpYfNLXU/IjIiIiIiIh0VC1kY7WShLSp+KIzTnkuDXM\nQf6kaw+s/1mGp/vk1v8o+RERERERkQHn87qp3PcmJrONzILFR2wXFZ/3xfqfbSe3/kfJj4iIiIiI\nDLia/WvwdreSNmI2YeFxR22bOmI2MUljcTXspabkvRO+ppIfEREREREZUN2dTdTuX4vNHkPqiPOP\n2b73+p+3cDXsPaHrKvkREREREZEBVbnvTQy/l8xRi7BYw/p0zsH1PyazhaLPn6CtqeS4r6vkJ0ju\nvfderrrqKr75zW+yatWqUIcjIiIiIjIotTeX0VjzOZExWSRkTDuuc6Pi88iftBS/4WPf53+n3VVx\nXOcr+QmCTz75hMLCQpYvX85jjz3GH//4x1CHJCIiIiIy6BiGQfmeVwHIGnMxJtPxpyNxKaeRN+Fq\n/F43+zY+RmdbTZ/P1SanQXDGGWcwadIkAGJiYujs7MQwjF51ykVERERETnUdrgraW0qJSxlPdHz+\nCfeTkD4Fv6+b0p3PsW/jY4w54wfYI5OOed6wSn5K/vcfNHy8Lqh9Jp4zg7wbrz9qG7PZTEREBADP\nPfccs2fPVuIjIiIiIvIVBwsVJKRNPem+krLOxOdzU7HnVfZueJQxZ/7wmFXjNO0tiFavXs2LL77I\n7373u1CHIiIiIiIy6PQkPyaiE0YFpb/U3HPJGLWQ7q4m9m54FI+79ajth9XIT96N1x9zlKa/fPDB\nBzz66KM8/vjjREVFhSQGEREREZHByud1095cSmRMJtYwR9D6Tcubi8/rpnb/GvZtfAzss4/Ydlgl\nP6HS1tbGn//8Z5544gmio6NDHY6IiIiIyKDT1lSMYfiISRwd1H5NJhOZBYvw+9zUl38M9iO3VfIT\nBG+88QbNzc3ccsstgUIH9957L2lpaaEOTURERERkUDi43ifYyQ/0JEDZYy/B7+umwX3kdkp+gmDJ\nkiUsWbIk1GGIiIiIiAxaroa9mM02HHG5/dK/yWQmd/wSGjZtOmIbFTwQEREREZF+1d3VTFd7HVEJ\nIzGb+2/85VgVl5X8iIiIiIhIv3I17AP6Z8rb8VDyIyIiIiIi/eqL9T4FIY2j35Of3bt3M3/+fJYt\nW3bIsWXLlnHVVVdx7bXXcvfdd/d3KCIiIiIiMsAMw09rwz5s9hjCHakhjaVfk5/Ozk7uueceZs6c\necixtrY2Hn/8cZ555hmWLVtGYWEhW7du7c9wRERERERkgHW2VuP1tBOTWHDMNTn9rV+TH7vdziOP\nPEJSUtIhx8LCwrDb7bS1teH1eunq6iI2NrY/wxERERERkQF2cMpbdIjX+0A/l7o2m82EhYUd9lhY\nWBg//vGPmTdvHuHh4Xz9618nN7d/yt71t66uLn71q1/R0NBAd3c3P/jBD5gzZ06owxIRERERCblA\nsYOE0K73gRDu89PW1sZDDz3E22+/jcPh4Prrr2fv3r2MHn30jHDjxo29no8fP74/w+yTd999l4kT\nJ/Kd73yHqqoqbrzxxkOSnx07doQmOAmKr37uRAaCPncSCvrcyUDTZ26YM7zQWASWOLZu3xvqaEKX\n/BQXF5OdnR2Y6jZ9+nS2b99+zORn+vTpgcddXV39GmNfLV68OPC4qqqK9PT0Q9qMHz+e8PDwgQxL\ngmTjxo29PnciA0GfOwkFfe5koOkzN/y5nHvY1+gnNWsyWWMG5nd9tIQ6ZMlPZmYmxcXFdHd3ExYW\nxvbt2znvvPNOqs9VK3ayc0tVkCLscdrkDOZffFqf2l511VXU1dXx8MMPBzUGEREREZGh6Iv9fUI/\n5Q36OfnZsmULv/3tb2lsbMRisbB8+XIuv/xysrKymDdvHt/5zndYunQpVquVqVOncvrpp/dnOP1u\n+fLl7N69m5/97Ge8+uqroQ5HRERERCSkXI17MZmtRMXnhzoUoJ+Tn8mTJ7NixYojHl+yZAlLliwJ\n2vXmX3xan0dpgmn79u0kJiaSnp7O2LFj8fl8NDY2kpCQMOCxiIiIiIgMBh53K52t1UQnFGC22EId\nDjAAm5yeCjZs2MD//u//AuB0Ouns7FTiIyIiIiKnNFfj4JryBkp+guLqq6+moaGBa6+9lptuuok7\n7rgj1CGJiIiIiIRU64H9fWIGwf4+B4Ws4MFwYrfb+c///M9QhyEiIiIiMigYhoGrYR9Wm4OI6EMr\nIYeKRn5ERERERCSoutpr8bhdRCcWYDINnpRj8EQiIiIiIiLDwhclrgfPlDdQ8iMiIiIiIkHmCqz3\nGTzFDkDJj4iIiIiIBJHf76WtsYhwRwph4XGhDqcXJT8iIiIiIhI07c2l+P2eQTflDZT8iIiIiIhI\nEB2c8hY9yKa8gZKfoHG73cyfP5+XX3451KGIiIiIiISMq2EfJpOF6PiRoQ7lEEp+guShhx4iLm5w\nzWkUERERERlIXk8HHa4KHLE5WKz2UIdzCCU/QVBcXExJSQmzZ88OdSgiIiIiIiHT1rwfMIhOGHyj\nPgDWUAcQTBV7XqOpdmtQ+4xPnUTWmIuO2ubee+/l9ttv58UXXwzqtUVERESOpqV+F57uVpIyzwx1\nKCIAtDUWAxAVnx/iSA5vWCU/ofDyyy9zxhlnkJGRAYBhGCGOSERERIY7w/BTXbSK6uLVAERGZxIZ\nkxniqESgtakYTGai4nJDHcphDavkJ2vMRcccpQm2tWvXUlFRwdtvv01NTQ12u520tDRmzJgxoHGI\niIjIqcHv87B/+z9pqt2CxRaJz9NBbela8iZeE+rQ5BTn87rpaK3EEZON2RIW6nAOa1glP6Fw3333\nBR4/8MADZGVlKfERERGRfuFxuyj8/Ak6XOVExeWRP/k69m54mMaaLWQWLB50G0rKqaW9pRQM/6Cd\n8gYqeCAiIiIyJHS0VrHrk/vpcJWTkD6dgtO/j80eRWrueWD4qSv7KNQhyimutalnvU90fF6IIzky\njfwE0c033xzqEERERGQYaq7bScm2Zfh93WSMWkRa3vmYTCYAEtKnUln4Js6K9aTnX4DFGh7iaOVU\n1dZUApiIihsR6lCOSCM/IiIiIoOUYRjU7l9L0eYnMAyD/MlLSc+fG0h8AMwWGynZ5+DzduGs/CyE\n0cqpzO/z0N5SRkR0BhZbRKjDOSIlPyIiIiKDVE3JGir2vobNHs2YM35AfOqkw7ZLzj4Hk9lGXekH\nGH7fAEcpAu2ucgy/d1BPeQMlPyIiIiKDktfTQU3Ju1jDohh71o9xxGYfsa01zEFixnS6u5ports+\ngFGK9GhrGtz7+xyk5EdERERkEKov/xi/z01q7uw+VXFLzT0PgNrS97XvYBC01O+mvaUs1GEMGa2B\n5EcjPyIiIiJyHHzebupKP8RijSA5++w+nRPuSCY2+TTaW8pob97fvwEOc43Vn1P4+ePs+exhOlur\nQx3OoGf4fbQ3lxLuSMUWFhXqcI5KyY+IiIjIIOOs/ASvp52UnJnHVb3ty6M/cmLamkrYv/2fmM02\nDL+Hoi3/h8/TGeqwBrWO1kr8vu5BP+oDKnUdFJ9++ik/+clPKCgowDAMxowZw29/+9tQhyUiIiJD\nkN/vpXb/WsxmGyk5s47r3Kj4fCJjsmiu20FXh5PwyKR+irJ/GIZBV3stzXU7aK7bTndnE6l5c0jN\nOReT2dLv1+9qr6dw8xMYGIyaegOuhn3U7n+P/TueJX/ydb2q7MkXvtjfZ3Cv9wElP0Fz5pln8te/\n/jXUYYiIiMgQ11i9CY+7hZTcc7GGOY7rXJPJRGrueZRse5q60g/IGXfpUdv7PJ14vZ1YrBFYrHZM\npoGfFGQYftqbS3sSnvoduDucPQdMZsyWMCr3vk5TzVZGjP8mEdHp/RaHt7udws//js/TQe5pVxCT\nOJro+JG0t5TTXLed2v1rScub02/XH8p69vcZ/Ot9QMlP0GhhoYiIiJwsw/BTU7IGk8kSmMJ2vOJT\nJ1Gx7w0aKj8jY9QCrLbIQ9p4u9up2f8edWUfYfg9B141YbGG9/zYIrBYI7DawrGFxxEekYQ9Mgm7\nIwl7ePxxjcIYhoHf58bT3Yb3wI+nux1vdxu07WHr2td6HgNmi5341EnEpUwgJmksGH7K97xKY/Um\ndq7/C+l5c0nLvwCzObhfYf1+L0Wb/4G7w0la3vkkZZ3Vc0fMFvInXcuu9X+hct8bOGKziU4YGdRr\nD3WG4aetqYSwiIQ+FeYItWGV/Dy3q4KNNc1B7XN6WhzfHJd1zHZFRUX88Ic/pKWlhR/96Eecc845\nQY1DREREhr/m2m24O5wkZZ55wl8kTWYLqTmzqNj7GvXl60jPvyBwzOftoq70A2pK38fv7cJmjyU6\nYSQ+b1fPj6cDr7cLd0cDfp/7SBfAHpHQkwxFJmLChM/Xjd/nxu/txudz4/d14/O68fvceLvbMYyj\n7D0UFkVS5lnEpYwnOmEUZout1+G8iVeTkDaF0l0vUl28mqbabeSO/yZRcbkndH++yjAMSrc/S1tz\nCfGpk8kYtbDXcZs9mvxJ32LPhocp3voU486+hbDw2KBcezjobKvB5+0kLmV8qEPpk2GV/IRKbm4u\nN998M4sWLaK8vJzrrruOVatWYbXq9oqIiEjfGIZBdcm7gInUEXNOqq+kzDOpKlpFXdlHpI6YDYZB\nfcU6aorfxetpx2pzkDHmYpKzZhySbATi8fvwebtwdzXhbnfi7qinq8OJu8PZ869z9xGvbzbbMFvt\nmC1hRERnYAuLwnrgxxbmCDwuLKpg0pnnH3O6XWzyOMbH/5TKfW9QX76OPZ8+SErOLDJGLcRiDTup\ne1Vd9DaNNZ/jiM1lxIQrDxtLVHweWaMvomLPqxRvfYoxp980IGuQhoKhsr/PQcPq2/k3x2X1aZQm\n2FJTU1m0aBEA2dnZJCUlUVtbS2Zm5oDHIiIiIkOTy7mHztYq4tMmE+5IPqm+LLYIkrLOoq70fUp3\nPE9rYyEedwtmazgZIy8kJffcY1aRM5ktWMMcWMMcOGIO/X7l9XTg7mzEhAmzJQzLgWTHbAnr+9qh\n0rY+t7VYw8kZdxnxaVMo3fEcdWUf0OLcxbiz/h8WW0TfrvcVzsrPqC5eTVhEAiOn3nDERBAgJWcW\n7S2lNNVsoWLf62SP+foJXXO4aT2w3id6CKz3AZW6DooVK1bwwAMPANDQ0EBjYyOpqakhjkpERESG\nkpqSdwFIy5sblP5ScmaByUxj9Ua8nnZSR8xh4qxfkT5y/nGVzz4Sqy0SR0wWkTGZhDuSsdljsFjD\n+71oQnR8PqfNuI2kzDNxdzipK/vwhPppbSykdOfzWKwRFEz7zjH3pzGZTOSe9k3CHSnUlX5AU82W\nE7rucGIYBm1NxdjssYRFJIY6nD5R8hMEc+fOZfv27Vx99dX86Ec/4ve//72mvImIiEiftTWV0NZc\nQkzSWCKjM4LSpz0inuwxXyc19zwmzPoVWaO/dtzV4wYrs8VG1pivY7FFUlv6AT5v13Gd7/O6Kd76\nNCZMjJxyPeGOlD6dZ7HayZ98HWaLnf07nqOrve5Ewh823B31eLvbiIrPGzJlwPUNPQgcDgcPP/xw\nqMMQERHZWMKpAAAgAElEQVSRIar6wKhPepBGfQ5KyZkZ1P4GE4vVTmrueVQVvkVd2cek5/f93tWW\nvo+3u5X0/HnHXb0tIiqV3PHfpGTrUxRt/j/Gnf2To06XG86+mPI2NNb7gEZ+REREREKqw1WJy7mb\nqPj8IbFPymCSkjMTizWC2tK1+LxHqE73FR53K7X738MaFtVTDOIEJKRNJjl7Jl3ttZTvefWE+hgO\nhlqxA1DyIyIiIhJSNSVrAEjLOz/EkQw9Fms4Kbnn4vN0UF/+cZ/OqS5ahd/XTXr+ya19yhr9NSKi\n03FWrKepZusJ9zOUtTUVY7U5+jxtcDBQ8iMiIiISIl3t9TTVbiUiOpOYxDGhDmdISsmZhcUaTu3+\ntfi83Udt29VeR33lJ9gjk0g+sJHpiTJbbORP+hZms43Snc/h7mw8qf6GGndnI91dzUNqvQ8o+RER\nEREJmZqSdwCD9Lzzh9QXyMHEaosgJWcWXk87zop1R21bue9NMPxkFiwOyj494Y4Ussddis/bRcnW\npzH8R9nMdZgZilPeQMmPiIiISEh0ttXSULWJiKg04lInhjqcIS0l91zMFjs1+9/D7/Mctk1b836a\n67bjiM0lLmVC0K6dmHE6CWlTaW8pparo7aD1O9gNtf19DlLyIyIiIhICVYVvAQYZoxb1+944w53V\nFklKzky83W3UV6w/5LhhGFTsfQ3oWasTzFE2k8lEzmmXYY9IpKZkDa6GvUHrezBrayrGbA0nIkil\n2QeK/pcWJK+++iqXXHIJl19+OWvXrg11OCIiIjKItbeUHxiFyCE2eVyowxkWUnPPw2wJo/Ywoz/N\nddtpby4lLmVCv1TUs1jDyZt0LSaTmZJty/G4W4N+jcHE43bh7nASFTdiyCXuQyvaQaq5uZkHH3yQ\n5cuX88gjj/DOO++EOiQREREZxHpGfSCzYJHW+gSJNcxBcvZMPG4XzspPA68bfl/PWh+TmcyCRf12\nfUdsNpkFi/B2t7J/+3IMw3/Etobhp7urZciuERqK+/scpE1Og+Djjz9m5syZREREEBERwZ133hnq\nkERERGSQam0swtWwl+iEAqITRoU6nGElNfc86ss+pKZkDUlZZ2E2W3FWfoK7o57krBn9XpI5Jfdc\nXI2FuJy7qS19n7QRczAMg+7ORtpdFXS4yuloqaC9tRK/twuz2UZkbDaO2BwcsTlExeVis8f0a4zB\n8EWxg6G13geGWfLz9xU7+GhLZVD7nDk5k29fPP6obSorK+ns7OQHP/gBra2t/OhHP2LGjBlBjUNE\nRESGPsMwqCx8E4DMgoUhjmb4sdmjSM6eQW3p+zRUfkZC+lSqilZhtoSRPnJ+v1/fZDIzYvyV7Fz3\nX1TuexOXcw8drkp83s5e7eyRyUQkjMLd4aStqSSQTACEhcfhiM0lKj6f5KyzglKVLtham4oxmW1E\nxmSFOpTjNqySn1AxDIPm5mYeeughKioquO6661izZk2owxIREZFBpsW568Dak/E4YnNCHc6wlDpi\nNnXlH1NT8i7dXU14u9tIH3khNnv0gFzfZo8ib+LV7Nv0P7Q2FmKPTCImaQyOmCwiY7KIjM7AYosI\ntPd5u2hvqaC9pZT2ljLam0tpqt1CU+0WfN4O0vPnDUjcfeVxu+hqqyE6oQCzeeilEkMv4qP49sXj\njzlK0x+SkpKYOnUqJpOJ7OxsHA4HjY2NJCQkDHgsIiIiMjgZhp+qfW8BJjJGatSnv9jsMSRnzaCu\n7ANqStZgDYsmNfe8AY0hJrGAibN+hdkajvVLic7hWKzhxCSOIiaxZwqkYRi4O5zs/vQBaks/CGzi\nOli4nD3V7GKThuamvCp4EAQzZ87kk08+wTAMmpqa6OjoUOIjIiIivTTVbKWzrZqE9KlERKeFOpxh\nLXXEbEwHRiUyRl2IxWof8BjCIuKPmfgcjslkItyRTGrOLHyeDurLj75x60BradgDQMwQTX6G1chP\nqKSmprJgwQKWLFmCyWTi9ttvD3VIIiIiMogYfh9VRSvBZCZj5IWhDmfYCwuPJbNgMe3NpSRlnBHq\ncE5Ics4sakrfp7b0fVJyZmK2hAWt74bKDXS0VZM1+qLjqjZoGH5cDXux2WMJd6QGLZ6B1O8jP7t3\n72b+/PksW7bskGM1NTVcc801LFmyhN///vf9HUq/WrJkCc899xzPPvssc+bMCXU4IiIiMog0VG3A\n3eEkOfMs7JGJoQ7nlJCaey75k781KAsG9IXVFkFK9sGNWz8JWr/Oik/Zv+Of1JW+T0fr8RUK63BV\n4PN0EJM0ZsiWaO/X5Kezs5N77rmHmTNnHvb4n/70J77zne/w7LPPYrFYqKmp6c9wRERERAac3+eh\nqmgVJrOVtPwLQh2ODCGpued+sXGr33vS/TVWf07pzufhwMakzbVbj+v8FmfPlLfYxKE55Q36Ofmx\n2+088sgjJCUlHXLMMAw2btzI3LlzAfjd735HWprmv4qIiMjwUl+xDo+7hZScmYSFx4Y6HBlCrGEO\nkrNm4HG7aKj87KT6aq7bQcn25ZitdsacfhNmSxhNtdswDKPPfbice8BkJjqx4KRiCaV+TX7MZjNh\nYYefn9jY2EhkZCR33XUX11xzDf/1X//Vn6GIiIiIDDift4ua4ncxW+ykjTg/1OHIEJQ64jxMZis1\nJWsw/L4T6sPVsJfiLU9iNlkomPYdouLziE0ah7vDSWdbdZ/68Ho6aG8pwxGbc0KFHAaLkBU8MAyD\nuro6brjhBjIyMvj+97/P2rVrmT179lHP27hxY6/n48cPfGnrE7Fjx45QhyAn4aufO5GBoM+dhII+\nd0HWuRc87RAxgS3bdoc6mkFJn7k+CMuju2sfm9a9COH5x3eupx5c7wEGRvRM9hQ1AA3gdgCwa/NK\niJx07H7cZYBBuztqSP/OQpb8xMfHk5mZSVZWz86wM2bMoLCw8JjJz/Tp0wOPu7q6+jXGYBo/fjzh\n4YOnRrv03caNG3t97kQGgj53Egr63AXf3g0baO2AiWd8Q1PeDkOfub7p7hrJ9g/+RJhRzPipl/W5\niEO7q4K9G17GbzIYOfl64lJOCxzzeSew5b3PCDPVM37atGMWMNi/o4iGNhg7aS6O2OyTej/97WjJ\nWcj2+bFYLGRlZVFWVgb0jIzk5eWFKhwRERGRoPJ6OmhtKiYyJluJj5yUsPA4EjPPwN3hpLF2S5/O\n6WytYd/Gx/B73eRNvLpX4gNgsdqJTRqDu6Oervbao/ZlGAYu5x6sNgeRMZkn/D4Gg34d+dmyZQu/\n/e1vaWxsxGKxsHz5ci6//HKysrKYN28ev/nNb/jVr36FYRiMHj06UPxgqHn++ed55ZVXMJlMGIbB\njh072LRpU6jDEhERkRByOfeA4T/kS6fIiUgbcT7Oyk+pKX6HhLQpmExHHsPobK1h78ZH8Xk6yB3/\nTRLSphy2XXzqJJrrttNUu5WIqCMXHutqq8HjdpGQNvWo1x0K+jX5mTx5MitWrDji8ZycHJ5++un+\nDGFAXHHFFVxxxRUAfPbZZ7z11lshjkhERERCrbl+JwCxyUp+5OTZIxNITJ9GQ9UGmmu3E5926Dqd\n7q5mqotW4az8DDDIHnsJSZl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lPhigajIySXvlNZoNBkpXr2PQvxah8XdsbS+J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\n9sMrL/5Bog6Rs3l5EtNuLzXtZt6qHiIyTE5afPiC349zZopfHHkYkSDiW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ozj1f7/hUwq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c10t82QnKbn+hs3onvDFGyFUkpuoYmqTVmo1DKsFg/ueR/l69KX+8Be5/D/Y8jCiGuJDxWn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\nZlXX8fj8PLi7Aavby4835M7593Ymx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pqMrC2MIeg0jR0DfIhIJEJvUJOZG8XK9SlExujw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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0167b97e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "returns = pd.DataFrame(np.random.normal(1.0, 0.03, (100, 10)))\n",
    "prices = returns.cumprod()\n",
    "prices.plot()\n",
    "plt.title('Randomly-generated Prices')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Price')\n",
    "plt.legend(loc=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's have a look at how we actually build up to this point!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas Data Structures\n",
    "\n",
    "### `Series`\n",
    "\n",
    "A pandas `Series` is a 1-dimensional array with labels that can contain any data type. We primarily use them for handling time series data. Creating a `Series` is as easy as calling `pandas.Series()` on a Python list or NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.0\n",
      "1    2.0\n",
      "2    NaN\n",
      "3    4.0\n",
      "4    5.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series([1, 2, np.nan, 4, 5])\n",
    "print s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every `Series` has a name. We can give the series a name as a parameter or we can define it afterwards by directly accessing the name attribute. In this case, we have given our time series no name so the attribute should be empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print s.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This name can be directly modified with no repercussions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toy Series\n"
     ]
    }
   ],
   "source": [
    "s.name = \"Toy Series\"\n",
    "print s.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We call the collected axis labels of a `Series` its index. An index can either passed to a `Series` as a parameter or added later, similarly to its name. In the absence of an index, a `Series` will simply contain an index composed of integers, starting at $0$, as in the case of our \"Toy Series\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=5, step=1)\n"
     ]
    }
   ],
   "source": [
    "print s.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas has a built-in function specifically for creating date indices, `date_range()`. We use the function here to create a new index for `s`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
      "               '2016-01-05'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "new_index = pd.date_range(\"2016-01-01\", periods=len(s), freq=\"D\")\n",
    "print new_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An index must be exactly the same length as the `Series` itself. Each index must match one-to-one with each element of the `Series`. Once this is satisfied, we can directly modify the `Series` index, as with the name, to use our new and more informative index (relatively speaking)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',\n",
      "               '2016-01-05'],\n",
      "              dtype='datetime64[ns]', freq='D')\n"
     ]
    }
   ],
   "source": [
    "s.index = new_index\n",
    "print s.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index of the `Series` is crucial for handling time series, which we will get into a little later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing `Series` Elements\n",
    "\n",
    "`Series` are typically accessed using the `iloc[]` and `loc[]` methods. We use `iloc[]` to access elements by integer index and we use `loc[]` to access the index of the Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First element of the series:  1.0\n",
      "Last element of the series:  5.0\n"
     ]
    }
   ],
   "source": [
    "print \"First element of the series: \", s.iloc[0]\n",
    "print \"Last element of the series: \", s.iloc[len(s)-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can slice a `Series` similarly to our favorite collections, Python lists and NumPy arrays. We use the colon operator to indicate the slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    1.0\n",
       "2016-01-02    2.0\n",
       "Freq: D, Name: Toy Series, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When creating a slice, we have the options of specifying a beginning, an end, and a step. The slice will begin at the start index, and take steps of size `step` until it passes the end index, not including the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    1.0\n",
       "2016-01-02    2.0\n",
       "2016-01-03    NaN\n",
       "2016-01-04    4.0\n",
       "Freq: D, Name: Toy Series, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = 0\n",
    "end = len(s) - 1\n",
    "step = 1\n",
    "\n",
    "s.iloc[start:end:step]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even reverse a `Series` by specifying a negative step size. Similarly, we can index the start and end with a negative integer value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-05    5.0\n",
       "2016-01-04    4.0\n",
       "2016-01-03    NaN\n",
       "2016-01-02    2.0\n",
       "2016-01-01    1.0\n",
       "Freq: -1D, Name: Toy Series, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a slice of the series that starts from the second to last element and ends at the third to last element (because the fourth to last is not included, taking steps of size $1$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-04    4.0\n",
       "2016-01-03    NaN\n",
       "Freq: -1D, Name: Toy Series, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[-2:-4:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access a series by using the values of its index. Since we indexed `s` with a collection of dates (`Timestamp` objects) we can look at the value contained in `s` for a particular date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['2016-01-01']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or even for a range of dates!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-02    2.0\n",
       "2016-01-03    NaN\n",
       "2016-01-04    4.0\n",
       "Freq: D, Name: Toy Series, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['2016-01-02':'2016-01-04']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With `Series`, we *can* just use the brackets (`[]`) to access elements, but this is not best practice. The brackets are ambiguous because they can be used to access `Series` (and `DataFrames`) using both index and integer values and the results will change based on context (especially with `DataFrames`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Indexing\n",
    "\n",
    "In addition to the above-mentioned access methods, you can filter `Series` using boolean arrays. `Series` are compatible with your standard comparators. Once compared with whatever condition you like, you get back yet another `Series`, this time filled with boolean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-01     True\n",
      "2016-01-02     True\n",
      "2016-01-03    False\n",
      "2016-01-04    False\n",
      "2016-01-05    False\n",
      "Freq: D, Name: Toy Series, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print s < 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass *this* `Series` back into the original `Series` to filter out only the elements for which our condition is `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-01    1.0\n",
      "2016-01-02    2.0\n",
      "Freq: D, Name: Toy Series, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print s.loc[s < 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we so desire, we can group multiple conditions together using the logical operators `&`, `|`, and `~` (and, or, and not, respectively)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-01-02    2.0\n",
      "Freq: D, Name: Toy Series, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print s.loc[(s < 3) & (s > 1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very convenient for getting only elements of a `Series` that fulfill specific criteria that we need. It gets even more convenient when we are handling `DataFrames`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Indexing and Time Series\n",
    "\n",
    "Since we use `Series` for handling time series, it's worth covering a little bit of how we handle the time component. For our purposes we use pandas `Timestamp` objects. Let's pull a full time series, complete with all the appropriate labels, by using our `get_pricing()` method. All data pulled with `get_pricing()` or using our Pipeline API will be in either `Series` or `DataFrame` format. We can modify this index however we like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = \"CMG\"\n",
    "start = \"2012-01-01\"\n",
    "end = \"2016-01-01\"\n",
    "prices = get_pricing(symbol, start_date=start, end_date=end, fields=\"price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display the first few elements of our series by using the `head()` method and specifying the number of elements that we want. The analogous method for the last few elements is `tail()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "Name: Equity(28016 [CMG]), dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print \"\\n\", type(prices)\n",
    "prices.head(5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with our toy example, we can specify a name for our time series, if only to clarify the name the `get_pricing()` provides us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old name:  Equity(28016 [CMG])\n",
      "New name:  CMG\n"
     ]
    }
   ],
   "source": [
    "print 'Old name: ', prices.name\n",
    "prices.name = symbol\n",
    "print 'New name: ', prices.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a closer look at the `DatetimeIndex` of our `prices` time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2012-01-03', '2012-01-04', '2012-01-05', '2012-01-06',\n",
      "               '2012-01-09', '2012-01-10', '2012-01-11', '2012-01-12',\n",
      "               '2012-01-13', '2012-01-17',\n",
      "               ...\n",
      "               '2015-12-17', '2015-12-18', '2015-12-21', '2015-12-22',\n",
      "               '2015-12-23', '2015-12-24', '2015-12-28', '2015-12-29',\n",
      "               '2015-12-30', '2015-12-31'],\n",
      "              dtype='datetime64[ns, UTC]', length=1006, freq=None)\n"
     ]
    }
   ],
   "source": [
    "print prices.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that this `DatetimeIndex` has a collection of associated information. In particular it has an associated frequency (`freq`) and an associated timezone (`tz`). The frequency indicates whether the data is daily vs monthly vs some other period while the timezone indicates what locale this index is relative to. We can modify all of this extra information!\n",
    "\n",
    "If we resample our `Series`, we can adjust the frequency of our data. We currently have daily data (excluding weekends) because `get_pricing()` pulls only data from market days. Let's up-sample from this daily data to monthly data using the `resample()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31 00:00:00+00:00    354.812125\n",
       "2012-02-29 00:00:00+00:00    379.582000\n",
       "2012-03-31 00:00:00+00:00    406.996164\n",
       "2012-04-30 00:00:00+00:00    422.818505\n",
       "2012-05-31 00:00:00+00:00    405.810177\n",
       "2012-06-30 00:00:00+00:00    403.061905\n",
       "2012-07-31 00:00:00+00:00    353.871424\n",
       "2012-08-31 00:00:00+00:00    294.513478\n",
       "2012-09-30 00:00:00+00:00    326.566316\n",
       "2012-10-31 00:00:00+00:00    276.545329\n",
       "Freq: M, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_prices = prices.resample('M')\n",
    "monthly_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `resample()` method defaults to using the mean of the lower level data to create the higher level data. We can specify how else we might want the up-sampling to be calculated by specifying the `how` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31 00:00:00+00:00    355.380\n",
       "2012-02-29 00:00:00+00:00    378.295\n",
       "2012-03-31 00:00:00+00:00    408.850\n",
       "2012-04-30 00:00:00+00:00    420.900\n",
       "2012-05-31 00:00:00+00:00    405.390\n",
       "2012-06-30 00:00:00+00:00    402.790\n",
       "2012-07-31 00:00:00+00:00    380.370\n",
       "2012-08-31 00:00:00+00:00    295.380\n",
       "2012-09-30 00:00:00+00:00    332.990\n",
       "2012-10-31 00:00:00+00:00    286.440\n",
       "Freq: M, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_prices_med = prices.resample('M', how='median')\n",
    "monthly_prices_med.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even specify how we want the calculation of the new period to be done. Here we create a `custom_resampler()` function that will return the first value of the period. In our specific case, this will return a `Series` where the monthly value is the first value of that month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31 00:00:00+00:00    340.98\n",
       "2012-02-29 00:00:00+00:00    370.84\n",
       "2012-03-31 00:00:00+00:00    394.58\n",
       "2012-04-30 00:00:00+00:00    418.65\n",
       "2012-05-31 00:00:00+00:00    419.78\n",
       "2012-06-30 00:00:00+00:00    397.14\n",
       "2012-07-31 00:00:00+00:00    382.97\n",
       "2012-08-31 00:00:00+00:00    280.60\n",
       "2012-09-30 00:00:00+00:00    285.91\n",
       "2012-10-31 00:00:00+00:00    316.13\n",
       "Freq: M, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def custom_resampler(array_like):\n",
    "    \"\"\" Returns the first value of the period \"\"\"\n",
    "    return array_like[0]\n",
    "\n",
    "first_of_month_prices = prices.resample('M', how=custom_resampler)\n",
    "first_of_month_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also adjust the timezone of a `Series` to adapt the time of real-world data. In our case, our time series is already localized to UTC, but let's say that we want to adjust the time to be 'US/Eastern'. In this case we use the `tz_convert()` method, since the time is already localized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-02 19:00:00-05:00    340.9800\n",
       "2012-01-03 19:00:00-05:00    348.7400\n",
       "2012-01-04 19:00:00-05:00    349.9900\n",
       "2012-01-05 19:00:00-05:00    348.9500\n",
       "2012-01-08 19:00:00-05:00    339.5225\n",
       "2012-01-09 19:00:00-05:00    340.7000\n",
       "2012-01-10 19:00:00-05:00    347.3300\n",
       "2012-01-11 19:00:00-05:00    347.8300\n",
       "2012-01-12 19:00:00-05:00    354.3900\n",
       "2012-01-16 19:00:00-05:00    353.6100\n",
       "Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eastern_prices = prices.tz_convert('US/Eastern')\n",
    "eastern_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the capacity for timezone and frequency management, each time series has a built-in `reindex()` method that we can use to realign the existing data according to a new set of index labels. If data does not exist for a particular label, the data will be filled with a placeholder value. This is typically `np.nan`, though we can provide a fill method.\n",
    "\n",
    "The data that we `get_pricing()` only includes market days. But what if we want prices for every single calendar day? This will include holidays and weekends, times when you normally cannot trade equities.  First let's create a new `DatetimeIndex` that contains all that we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',\n",
      "               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',\n",
      "               '2012-01-09', '2012-01-10',\n",
      "               ...\n",
      "               '2015-12-23', '2015-12-24', '2015-12-25', '2015-12-26',\n",
      "               '2015-12-27', '2015-12-28', '2015-12-29', '2015-12-30',\n",
      "               '2015-12-31', '2016-01-01'],\n",
      "              dtype='datetime64[ns, UTC]', length=1462, freq='D')\n"
     ]
    }
   ],
   "source": [
    "calendar_dates = pd.date_range(start=start, end=end, freq='D', tz='UTC')\n",
    "print calendar_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use this new set of dates to reindex our time series. We tell the function that the fill method that we want is `ffill`. This denotes \"forward fill\". Any `NaN` values will be filled by the *last value* listed. So the price on the weekend or on a holiday will be listed as the price on the last market day that we know about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00+00:00         NaN\n",
       "2012-01-02 00:00:00+00:00         NaN\n",
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-07 00:00:00+00:00    348.9500\n",
       "2012-01-08 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "2012-01-10 00:00:00+00:00    340.7000\n",
       "2012-01-11 00:00:00+00:00    347.3300\n",
       "2012-01-12 00:00:00+00:00    347.8300\n",
       "2012-01-13 00:00:00+00:00    354.3900\n",
       "2012-01-14 00:00:00+00:00    354.3900\n",
       "2012-01-15 00:00:00+00:00    354.3900\n",
       "Freq: D, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar_prices = prices.reindex(calendar_dates, method='ffill')\n",
    "calendar_prices.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that we still have a couple of `NaN` values right at the beginning of our time series. This is because the first of January in 2012 was a Sunday and the second was a market holiday! Because these are the earliest data points and we don't have any information from before them, they cannot be forward-filled. We will take care of these `NaN` values in the next section, when we deal with missing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Data\n",
    "\n",
    "Whenever we deal with real data, there is a very real possibility of encountering missing values. Real data is riddled with holes and pandas provides us with ways to handle them. Sometimes resampling or reindexing can create `NaN` values. Fortunately, pandas provides us with ways to handle them. We have two primary means of coping with missing data. The first of these is filling in the missing data with  `fillna()`. For example, say that we want to fill in the missing days with the mean price of all days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00+00:00    501.062621\n",
       "2012-01-02 00:00:00+00:00    501.062621\n",
       "2012-01-03 00:00:00+00:00    340.980000\n",
       "2012-01-04 00:00:00+00:00    348.740000\n",
       "2012-01-05 00:00:00+00:00    349.990000\n",
       "2012-01-06 00:00:00+00:00    348.950000\n",
       "2012-01-07 00:00:00+00:00    348.950000\n",
       "2012-01-08 00:00:00+00:00    348.950000\n",
       "2012-01-09 00:00:00+00:00    339.522500\n",
       "2012-01-10 00:00:00+00:00    340.700000\n",
       "Freq: D, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanfilled_prices = calendar_prices.fillna(calendar_prices.mean())\n",
    "meanfilled_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `fillna()` is fairly easy. It is just a matter of indicating the value that you want to fill the spaces with. Unfortunately, this particular case doesn't make a whole lot of sense, for reasons discussed in the [lecture on stationarity](https://www.quantopian.com/lectures/integration-cointegration-and-stationarity) in the Lecture series. We could fill them with with $0$, simply, but that's similarly uninformative.\n",
    "\n",
    "Rather than filling in specific values, we can use the `method` parameter, similarly to how the `reindex()` method works. We could use \"backward fill\", where `NaN`s are filled with the *next* filled value (instead of forward fill's *last* filled value) like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01 00:00:00+00:00    340.9800\n",
       "2012-01-02 00:00:00+00:00    340.9800\n",
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-07 00:00:00+00:00    348.9500\n",
       "2012-01-08 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "2012-01-10 00:00:00+00:00    340.7000\n",
       "Freq: D, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfilled_prices = calendar_prices.fillna(method='bfill')\n",
    "bfilled_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But again, this is a bad idea for the same reasons as the previous option. Both of these so-called solutions take into account *future data* that was not available at the time of the data points that we are trying to fill. In the case of using the mean or the median, these summary statistics are calculated by taking into account the entire time series. Backward filling is equivalent to saying that the price of a particular security today, right now, tomorrow's price. This also makes no sense. These two options are both examples of look-ahead bias, using data that would be unknown or unavailable at the desired time, and should be avoided.\n",
    "\n",
    "Our next option is significantly more appealing. We could simply drop the missing data using the `dropna()` method. This is much better alternative than filling `NaN` values in with arbitrary numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-07 00:00:00+00:00    348.9500\n",
       "2012-01-08 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "2012-01-10 00:00:00+00:00    340.7000\n",
       "2012-01-11 00:00:00+00:00    347.3300\n",
       "2012-01-12 00:00:00+00:00    347.8300\n",
       "Freq: D, Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_prices = calendar_prices.dropna()\n",
    "dropped_prices.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our time series is cleaned for the calendar year, with all of our `NaN` values properly handled. It is time to talk about how to actually do time series analysis with pandas data structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time Series Analysis with pandas\n",
    "\n",
    "Let's do some basic time series analysis on our original prices. Each pandas `Series` has a built-in plotting method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g8QqYPSkfADB7Uh7uXD4Deq0KT7xSgZZ2G2wON+5++nPUNJrl50lBl5pT44iIiIhoODCZ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\niIiIiIj4nP8DPGDRCLQ+7OEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0167bf6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot();\n",
    "# We still need to add the axis labels and title ourselves\n",
    "plt.title(symbol + \" Prices\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Date\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as some built-in descriptive statistics. We can either calculate these individually or using the `describe()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  501.64121332\n",
      "Standard deviation:  146.700132549\n"
     ]
    }
   ],
   "source": [
    "print \"Mean: \", prices.mean()\n",
    "print \"Standard deviation: \", prices.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n",
      "count    1006.000000\n",
      "mean      501.641213\n",
      "std       146.700133\n",
      "min       236.240000\n",
      "25%       371.605000\n",
      "50%       521.130000\n",
      "75%       646.810000\n",
      "max       757.770000\n",
      "Name: CMG, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print \"Summary Statistics\"\n",
    "print prices.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily modify `Series` with scalars using our basic mathematical operators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    671.960\n",
       "2012-01-04 00:00:00+00:00    687.480\n",
       "2012-01-05 00:00:00+00:00    689.980\n",
       "2012-01-06 00:00:00+00:00    687.900\n",
       "2012-01-09 00:00:00+00:00    669.045\n",
       "Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified_prices = prices * 2 - 10\n",
    "modified_prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can create linear combinations of `Series` themselves using the basic mathematical operators. pandas will group up matching indices and perform the calculations elementwise to produce a new `Series`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    371.013281\n",
       "2012-01-04 00:00:00+00:00    357.417023\n",
       "2012-01-05 00:00:00+00:00    344.953572\n",
       "2012-01-06 00:00:00+00:00    407.123572\n",
       "2012-01-09 00:00:00+00:00    305.081540\n",
       "dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_prices = prices + 5 * pd.Series(np.random.normal(0, 5, len(prices)), index=prices.index) + 20\n",
    "noisy_prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are no matching indices, however, we may get an empty `Series` in return."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00   NaN\n",
       "2012-01-04 00:00:00+00:00   NaN\n",
       "2012-01-05 00:00:00+00:00   NaN\n",
       "2012-01-06 00:00:00+00:00   NaN\n",
       "2012-01-09 00:00:00+00:00   NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_series = prices + pd.Series(np.random.normal(0, 1, len(prices)))\n",
    "empty_series.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than looking at a time series itself, we may want to look at its first-order differences or percent change (in order to get additive or multiplicative returns, in our particular case). Both of these are built-in methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "add_returns = prices.diff()[1:]\n",
    "mult_returns = prices.pct_change()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WiAuH9EtJGexMIBteZgoN2a0rYwWf9G87uqnJj1YsbfT4G5sBAP961TBc/3/O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\n3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IuxTdPwmJolG+76/Cz09AUUY4kzGGrKs1QvqxXb2NlLq/Hh9nacvaRa+33KWuJZ/ei/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\nT5fsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0164181c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Multiplicative returns of \" + symbol)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Percent Returns\")\n",
    "mult_returns.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas has convenient functions for calculating rolling means and standard deviations, as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolling_mean = pd.rolling_mean(prices, 30)\n",
    "rolling_mean.name = \"30-day rolling mean\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ymdh7DH6u1oDHx8PJvlaUm4uWa8f2plZvIj46oNNjevCmoR3e1xYINV6E18/V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\nIiJeR42QiIiIiIh4HTVCIiIiIiLidf4/kV5+6cMtaZIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01672d1f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot()\n",
    "rolling_mean.plot()\n",
    "plt.title(symbol + \"Price\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rolling_std = pd.rolling_std(prices, 30)\n",
    "rolling_std.name = \"30-day rolling volatility\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6GvDsZ8BDZGnRPD08DrsNM6fV4WB3CIpi/V1IIqJKSiblgkvanA61zyclWz/g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\nooCHEEIIIYQQErQo4CGEEEIIIYQErf8P0iYBli4KATIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01641a8f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_std.plot()\n",
    "plt.title(rolling_std.name);\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Standard Deviation\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many NumPy functions will work on `Series` the same way that they work on 1-dimensional NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000194158599839\n"
     ]
    }
   ],
   "source": [
    "print np.median(mult_returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of these functions, however, are already implemented directly as `Series` and `DataFrame` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000194158599839\n"
     ]
    }
   ],
   "source": [
    "print mult_returns.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In every case, using the built-in pandas method will be better than using the NumPy function on a pandas data structure due to improvements in performance. Make sure to check out the `Series` [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html) before resorting to other calculations of common functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `DataFrames`\n",
    "\n",
    "Many of the aspects of working with `Series` carry over into `DataFrames`. pandas `DataFrames` allow us to easily manage our data with their intuitive structure. \n",
    "\n",
    "Like `Series`, `DataFrames` can hold multiple types of data, but `DataFrames` are 2-dimensional objects, unlike `Series`. Each `DataFrame` has an index and a columns attribute, which we will cover more in-depth when we start actually playing with an object. The index attribute is like the index of a `Series`, though indices in pandas have some extra features that we will unfortunately not be able to cover here. If you are interested in this, check out the [pandas documentation](http://pandas.pydata.org/pandas-docs/version/0.18.1/advanced.html) on advanced indexing. The columns attribute is what provides the second dimension of our `DataFrames`, allowing us to combine named columns (all `Series`), into a cohesive object with the index lined-up.\n",
    "\n",
    "We can create a `DataFrame` by calling `pandas.DataFrame()` on a dictionary or NumPy `ndarray`. We can also concatenate a group of pandas `Series` into a `DataFrame` using `pandas.concat()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': [1, 2, 3, 4, 5], 'c': array([ 0.23752859, -0.49607459, -0.81027968, -0.28216034,  0.89001851]), 'b': ['L', 'K', 'J', 'M', 'Z']}\n"
     ]
    }
   ],
   "source": [
    "dict_data = {\n",
    "    'a' : [1, 2, 3, 4, 5],\n",
    "    'b' : ['L', 'K', 'J', 'M', 'Z'],\n",
    "    'c' : np.random.normal(0, 1, 5)\n",
    "}\n",
    "print dict_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `DataFrame` has a few key attributes that we need to keep in mind. The first of these is the index attribute. We can easily include an index of `Timestamp` objects like we did with `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            a  b         c\n",
      "2016-01-01  1  L  0.237529\n",
      "2016-01-02  2  K -0.496075\n",
      "2016-01-03  3  J -0.810280\n",
      "2016-01-04  4  M -0.282160\n",
      "2016-01-05  5  Z  0.890019\n"
     ]
    }
   ],
   "source": [
    "frame_data = pd.DataFrame(dict_data, index=pd.date_range('2016-01-01', periods=5))\n",
    "print frame_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, we can combine `Series` into `DataFrames`. Concatatenating `Series` like this will match elements up based on their corresponding index. As the following `Series` do not have an index assigned, they each default to an integer index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Evens  Odds\n",
      "0      2     1\n",
      "1      4     3\n",
      "2      6     5\n",
      "3      8     7\n",
      "4     10     9\n"
     ]
    }
   ],
   "source": [
    "s_1 = pd.Series([2, 4, 6, 8, 10], name='Evens')\n",
    "s_2 = pd.Series([1, 3, 5, 7, 9], name=\"Odds\")\n",
    "numbers = pd.concat([s_1, s_2], axis=1)\n",
    "print numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use `pandas.concat()` again later to combine multiple `DataFrame`s into one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `DataFrame` also has a `columns` attribute. These can either be assigned when we call `pandas.DataFrame` or they can be modified directly like the index. Note that when we concatenated the two `Series` above, the column names were the names of those `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'Evens', u'Odds'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print numbers.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To modify the columns after object creation, we need only do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shmevens  Shmodds\n",
      "0         2        1\n",
      "1         4        3\n",
      "2         6        5\n",
      "3         8        7\n",
      "4        10        9\n"
     ]
    }
   ],
   "source": [
    "numbers.columns = ['Shmevens', 'Shmodds']\n",
    "print numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same vein, the index of a `DataFrame` can be changed after the fact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=5, step=1)\n"
     ]
    }
   ],
   "source": [
    "print numbers.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Shmevens  Shmodds\n",
      "2016-01-01         2        1\n",
      "2016-01-02         4        3\n",
      "2016-01-03         6        5\n",
      "2016-01-04         8        7\n",
      "2016-01-05        10        9\n"
     ]
    }
   ],
   "source": [
    "numbers.index = pd.date_range(\"2016-01-01\", periods=len(numbers))\n",
    "print numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separate from the columns and index of a `DataFrame`, we can also directly access the values they contain by looking at the values attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  1],\n",
       "       [ 4,  3],\n",
       "       [ 6,  5],\n",
       "       [ 8,  7],\n",
       "       [10,  9]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This returns a NumPy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<type 'numpy.ndarray'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(numbers.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing `DataFrame` elements\n",
    "\n",
    "Again we see a lot of carryover from `Series` in how we access the elements of `DataFrames`. The key sticking point here is that everything has to take into account multiple dimensions now. The main way that this happens is through the access of the columns of a `DataFrame`, either individually or in groups. We can do this either by directly accessing the attributes or by using the methods we already are familiar with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = [\"CMG\", \"MCD\", \"SHAK\", \"WFM\"]\n",
    "start = \"2012-01-01\"\n",
    "end = \"2016-01-01\"\n",
    "prices = get_pricing(symbol, start_date=start, end_date=end, fields=\"price\")\n",
    "if isinstance(symbol, list):\n",
    "    prices.columns = map(lambda x: x.symbol, prices.columns)\n",
    "else:\n",
    "    prices.name = symbol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we directly access the `CMG` column. Note that this style of access will only work if your column name has no spaces or unfriendly characters in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.CMG.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use `loc[]` to access an individual column like so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    340.9800\n",
       "2012-01-04 00:00:00+00:00    348.7400\n",
       "2012-01-05 00:00:00+00:00    349.9900\n",
       "2012-01-06 00:00:00+00:00    348.9500\n",
       "2012-01-09 00:00:00+00:00    339.5225\n",
       "Name: CMG, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc[:, 'CMG'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing an individual column will return a `Series`, regardless of how we get it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print type(prices.CMG)\n",
    "print type(prices.loc[:, 'CMG'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we pass a tuple into the `loc[]` method? This is a key difference between accessing a `Series` and accessing a `DataFrame`, grounded in the fact that a `DataFrame` has multiple dimensions. When you pass a 2-dimensional tuple into a `DataFrame`, the first element of the tuple is applied to the rows and the second is applied to the columns. So, to break it down, the above line of code tells the `DataFrame` to return every single row of the column with label `'CMG'`. Lists of columns are also supported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>340.9800</td>\n",
       "      <td>98.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>348.7400</td>\n",
       "      <td>99.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>349.9900</td>\n",
       "      <td>99.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>348.9500</td>\n",
       "      <td>100.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
       "      <td>99.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc[:, ['CMG', 'MCD']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also simply access the `DataFrame` by index value using `loc[]`, as with `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-15 00:00:00+00:00</th>\n",
       "      <td>555.6401</td>\n",
       "      <td>116.96</td>\n",
       "      <td>41.5101</td>\n",
       "      <td>32.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16 00:00:00+00:00</th>\n",
       "      <td>568.4200</td>\n",
       "      <td>117.84</td>\n",
       "      <td>40.1400</td>\n",
       "      <td>33.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17 00:00:00+00:00</th>\n",
       "      <td>554.9399</td>\n",
       "      <td>117.56</td>\n",
       "      <td>38.5300</td>\n",
       "      <td>33.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18 00:00:00+00:00</th>\n",
       "      <td>540.7500</td>\n",
       "      <td>116.58</td>\n",
       "      <td>39.3800</td>\n",
       "      <td>32.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21 00:00:00+00:00</th>\n",
       "      <td>521.2300</td>\n",
       "      <td>117.71</td>\n",
       "      <td>38.2050</td>\n",
       "      <td>32.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22 00:00:00+00:00</th>\n",
       "      <td>495.2001</td>\n",
       "      <td>117.71</td>\n",
       "      <td>39.7600</td>\n",
       "      <td>34.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD     SHAK    WFM\n",
       "2015-12-15 00:00:00+00:00  555.6401  116.96  41.5101  32.96\n",
       "2015-12-16 00:00:00+00:00  568.4200  117.84  40.1400  33.66\n",
       "2015-12-17 00:00:00+00:00  554.9399  117.56  38.5300  33.38\n",
       "2015-12-18 00:00:00+00:00  540.7500  116.58  39.3800  32.72\n",
       "2015-12-21 00:00:00+00:00  521.2300  117.71  38.2050  32.98\n",
       "2015-12-22 00:00:00+00:00  495.2001  117.71  39.7600  34.78"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc['2015-12-15':'2015-12-22']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plays nicely with lists of columns, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-15 00:00:00+00:00</th>\n",
       "      <td>555.6401</td>\n",
       "      <td>116.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16 00:00:00+00:00</th>\n",
       "      <td>568.4200</td>\n",
       "      <td>117.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17 00:00:00+00:00</th>\n",
       "      <td>554.9399</td>\n",
       "      <td>117.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18 00:00:00+00:00</th>\n",
       "      <td>540.7500</td>\n",
       "      <td>116.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21 00:00:00+00:00</th>\n",
       "      <td>521.2300</td>\n",
       "      <td>117.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22 00:00:00+00:00</th>\n",
       "      <td>495.2001</td>\n",
       "      <td>117.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD\n",
       "2015-12-15 00:00:00+00:00  555.6401  116.96\n",
       "2015-12-16 00:00:00+00:00  568.4200  117.84\n",
       "2015-12-17 00:00:00+00:00  554.9399  117.56\n",
       "2015-12-18 00:00:00+00:00  540.7500  116.58\n",
       "2015-12-21 00:00:00+00:00  521.2300  117.71\n",
       "2015-12-22 00:00:00+00:00  495.2001  117.71"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc['2015-12-15':'2015-12-22', ['CMG', 'MCD']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `iloc[]` also works similarly, allowing you to access parts of the `DataFrame` by integer index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-03 00:00:00+00:00    98.81\n",
       "2012-01-04 00:00:00+00:00    99.42\n",
       "Name: MCD, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.iloc[0:2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>348.74</td>\n",
       "      <td>35.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>348.95</td>\n",
       "      <td>36.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10 00:00:00+00:00</th>\n",
       "      <td>340.70</td>\n",
       "      <td>36.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12 00:00:00+00:00</th>\n",
       "      <td>347.83</td>\n",
       "      <td>35.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17 00:00:00+00:00</th>\n",
       "      <td>353.61</td>\n",
       "      <td>38.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19 00:00:00+00:00</th>\n",
       "      <td>358.10</td>\n",
       "      <td>38.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23 00:00:00+00:00</th>\n",
       "      <td>360.53</td>\n",
       "      <td>38.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25 00:00:00+00:00</th>\n",
       "      <td>363.28</td>\n",
       "      <td>38.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27 00:00:00+00:00</th>\n",
       "      <td>366.80</td>\n",
       "      <td>37.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31 00:00:00+00:00</th>\n",
       "      <td>367.58</td>\n",
       "      <td>37.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-02 00:00:00+00:00</th>\n",
       "      <td>362.64</td>\n",
       "      <td>37.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-06 00:00:00+00:00</th>\n",
       "      <td>371.65</td>\n",
       "      <td>38.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-08 00:00:00+00:00</th>\n",
       "      <td>373.81</td>\n",
       "      <td>38.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-10 00:00:00+00:00</th>\n",
       "      <td>376.39</td>\n",
       "      <td>40.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-14 00:00:00+00:00</th>\n",
       "      <td>379.14</td>\n",
       "      <td>40.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-16 00:00:00+00:00</th>\n",
       "      <td>381.91</td>\n",
       "      <td>40.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-21 00:00:00+00:00</th>\n",
       "      <td>383.86</td>\n",
       "      <td>40.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-23 00:00:00+00:00</th>\n",
       "      <td>386.82</td>\n",
       "      <td>40.520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-27 00:00:00+00:00</th>\n",
       "      <td>389.11</td>\n",
       "      <td>40.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-29 00:00:00+00:00</th>\n",
       "      <td>390.47</td>\n",
       "      <td>40.370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CMG     WFM\n",
       "2012-01-04 00:00:00+00:00  348.74  35.725\n",
       "2012-01-06 00:00:00+00:00  348.95  36.435\n",
       "2012-01-10 00:00:00+00:00  340.70  36.335\n",
       "2012-01-12 00:00:00+00:00  347.83  35.935\n",
       "2012-01-17 00:00:00+00:00  353.61  38.390\n",
       "2012-01-19 00:00:00+00:00  358.10  38.665\n",
       "2012-01-23 00:00:00+00:00  360.53  38.060\n",
       "2012-01-25 00:00:00+00:00  363.28  38.575\n",
       "2012-01-27 00:00:00+00:00  366.80  37.445\n",
       "2012-01-31 00:00:00+00:00  367.58  37.015\n",
       "2012-02-02 00:00:00+00:00  362.64  37.800\n",
       "2012-02-06 00:00:00+00:00  371.65  38.205\n",
       "2012-02-08 00:00:00+00:00  373.81  38.960\n",
       "2012-02-10 00:00:00+00:00  376.39  40.805\n",
       "2012-02-14 00:00:00+00:00  379.14  40.495\n",
       "2012-02-16 00:00:00+00:00  381.91  40.300\n",
       "2012-02-21 00:00:00+00:00  383.86  40.285\n",
       "2012-02-23 00:00:00+00:00  386.82  40.520\n",
       "2012-02-27 00:00:00+00:00  389.11  40.825\n",
       "2012-02-29 00:00:00+00:00  390.47  40.370"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access prices with integer index in\n",
    "# [1, 3, 5, 7, 9, 11, 13, ..., 99]\n",
    "# and in column 0 or 3\n",
    "prices.iloc[[1, 3, 5] + range(7, 100, 2), [0, 3]].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean indexing\n",
    "\n",
    "As with `Series`, sometimes we want to filter a `DataFrame` according to a set of criteria. We do this by indexing our `DataFrame` with boolean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>340.9800</td>\n",
       "      <td>98.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>348.7400</td>\n",
       "      <td>99.42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>349.9900</td>\n",
       "      <td>99.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>348.9500</td>\n",
       "      <td>100.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
       "      <td>99.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD  SHAK     WFM\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81   NaN  34.810\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42   NaN  35.725\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83   NaN  36.370\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59   NaN  36.435\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62   NaN  36.440"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc[prices.MCD > prices.WFM].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add multiple boolean conditions by using the logical operators `&`, `|`, and `~` (and, or, and not, respectively) again!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-30 00:00:00+00:00</th>\n",
       "      <td>709.58</td>\n",
       "      <td>92.42</td>\n",
       "      <td>45.80</td>\n",
       "      <td>52.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02 00:00:00+00:00</th>\n",
       "      <td>712.55</td>\n",
       "      <td>92.49</td>\n",
       "      <td>43.50</td>\n",
       "      <td>53.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03 00:00:00+00:00</th>\n",
       "      <td>726.07</td>\n",
       "      <td>93.92</td>\n",
       "      <td>44.87</td>\n",
       "      <td>53.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 00:00:00+00:00</th>\n",
       "      <td>676.00</td>\n",
       "      <td>94.02</td>\n",
       "      <td>41.32</td>\n",
       "      <td>53.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05 00:00:00+00:00</th>\n",
       "      <td>670.62</td>\n",
       "      <td>94.32</td>\n",
       "      <td>42.46</td>\n",
       "      <td>53.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CMG    MCD   SHAK    WFM\n",
       "2015-01-30 00:00:00+00:00  709.58  92.42  45.80  52.10\n",
       "2015-02-02 00:00:00+00:00  712.55  92.49  43.50  53.15\n",
       "2015-02-03 00:00:00+00:00  726.07  93.92  44.87  53.41\n",
       "2015-02-04 00:00:00+00:00  676.00  94.02  41.32  53.67\n",
       "2015-02-05 00:00:00+00:00  670.62  94.32  42.46  53.38"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.loc[(prices.MCD > prices.WFM) & ~prices.SHAK.isnull()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding, Removing Columns, Combining `DataFrames`/`Series`\n",
    "\n",
    "It is all well and good when you already have a `DataFrame` filled with data, but it is also important to be able to add to the data that you have.\n",
    "\n",
    "We add a new column simply by assigning data to a column that does not already exist. Here we use the `.loc[:, 'COL_NAME']` notation and store the output of `get_pricing()` (which returns a pandas `Series` if we only pass one security) there. This is the method that we would use to add a `Series` to an existing `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "      <th>TSLA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>35.725</td>\n",
       "      <td>27.71</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>349.9900</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>36.370</td>\n",
       "      <td>27.12</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
       "      <td>99.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.440</td>\n",
       "      <td>27.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD  SHAK     WFM   TSLA\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81   NaN  34.810  28.06\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42   NaN  35.725  27.71\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83   NaN  36.370  27.12\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59   NaN  36.435  26.94\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62   NaN  36.440  27.21"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_1 = get_pricing('TSLA', start_date=start, end_date=end, fields='price')\n",
    "prices.loc[:, 'TSLA'] = s_1\n",
    "prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also just as easy to remove a column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>340.9800</td>\n",
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       "      <th>2012-01-04 00:00:00+00:00</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>35.725</td>\n",
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       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>349.9900</td>\n",
       "      <td>99.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>348.9500</td>\n",
       "      <td>100.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
       "      <td>99.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD  SHAK     WFM\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81   NaN  34.810\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42   NaN  35.725\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83   NaN  36.370\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59   NaN  36.435\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62   NaN  36.440"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = prices.drop('TSLA', axis=1)\n",
    "prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we instead want to combine multiple `DataFrame`s into one, we use the `pandas.concat()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Equity(8554 [SPY])</th>\n",
       "      <th>Equity(38054 [VXX])</th>\n",
       "      <th>Equity(5061 [MSFT])</th>\n",
       "      <th>Equity(24 [AAPL])</th>\n",
       "      <th>Equity(46631 [GOOG])</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>127.59</td>\n",
       "      <td>2154.88</td>\n",
       "      <td>26.83</td>\n",
       "      <td>58.730</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>127.68</td>\n",
       "      <td>2111.36</td>\n",
       "      <td>27.39</td>\n",
       "      <td>59.064</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>128.06</td>\n",
       "      <td>2065.28</td>\n",
       "      <td>27.67</td>\n",
       "      <td>59.710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>127.79</td>\n",
       "      <td>2032.00</td>\n",
       "      <td>28.12</td>\n",
       "      <td>60.351</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>128.00</td>\n",
       "      <td>2002.56</td>\n",
       "      <td>27.74</td>\n",
       "      <td>60.248</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Equity(8554 [SPY])  Equity(38054 [VXX])  \\\n",
       "2012-01-03 00:00:00+00:00              127.59              2154.88   \n",
       "2012-01-04 00:00:00+00:00              127.68              2111.36   \n",
       "2012-01-05 00:00:00+00:00              128.06              2065.28   \n",
       "2012-01-06 00:00:00+00:00              127.79              2032.00   \n",
       "2012-01-09 00:00:00+00:00              128.00              2002.56   \n",
       "\n",
       "                           Equity(5061 [MSFT])  Equity(24 [AAPL])  \\\n",
       "2012-01-03 00:00:00+00:00                26.83             58.730   \n",
       "2012-01-04 00:00:00+00:00                27.39             59.064   \n",
       "2012-01-05 00:00:00+00:00                27.67             59.710   \n",
       "2012-01-06 00:00:00+00:00                28.12             60.351   \n",
       "2012-01-09 00:00:00+00:00                27.74             60.248   \n",
       "\n",
       "                           Equity(46631 [GOOG])  \n",
       "2012-01-03 00:00:00+00:00                   NaN  \n",
       "2012-01-04 00:00:00+00:00                   NaN  \n",
       "2012-01-05 00:00:00+00:00                   NaN  \n",
       "2012-01-06 00:00:00+00:00                   NaN  \n",
       "2012-01-09 00:00:00+00:00                   NaN  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = get_pricing(['SPY', 'VXX'], start_date=start, end_date=end, fields='price')\n",
    "df_2 = get_pricing(['MSFT', 'AAPL', 'GOOG'], start_date=start, end_date=end, fields='price')\n",
    "df_3 = pd.concat([df_1, df_2], axis=1)\n",
    "df_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing data (again)\n",
    "Bringing real-life data into a `DataFrame` brings us the same problems that we had with it in a `Series`, only this time in more dimensions. We have access to the same methods as with `Series`, as demonstrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
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       "      <td>35.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
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       "      <th>2012-01-06 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD  SHAK     WFM\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81   0.0  34.810\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42   0.0  35.725\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83   0.0  36.370\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59   0.0  36.435\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62   0.0  36.440"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filled0_prices = prices.fillna(0)\n",
    "filled0_prices.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>340.9800</td>\n",
       "      <td>98.81</td>\n",
       "      <td>45.8</td>\n",
       "      <td>34.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>349.9900</td>\n",
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       "      <td>45.8</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>348.9500</td>\n",
       "      <td>100.59</td>\n",
       "      <td>45.8</td>\n",
       "      <td>36.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>339.5225</td>\n",
       "      <td>99.62</td>\n",
       "      <td>45.8</td>\n",
       "      <td>36.440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG     MCD  SHAK     WFM\n",
       "2012-01-03 00:00:00+00:00  340.9800   98.81  45.8  34.810\n",
       "2012-01-04 00:00:00+00:00  348.7400   99.42  45.8  35.725\n",
       "2012-01-05 00:00:00+00:00  349.9900   99.83  45.8  36.370\n",
       "2012-01-06 00:00:00+00:00  348.9500  100.59  45.8  36.435\n",
       "2012-01-09 00:00:00+00:00  339.5225   99.62  45.8  36.440"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bfilled_prices = prices.fillna(method='bfill')\n",
    "bfilled_prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But again, the best choice in this case (since we are still using time series data, handling multiple time series at once) is still to simply drop the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-30 00:00:00+00:00</th>\n",
       "      <td>709.58</td>\n",
       "      <td>92.42</td>\n",
       "      <td>45.80</td>\n",
       "      <td>52.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02 00:00:00+00:00</th>\n",
       "      <td>712.55</td>\n",
       "      <td>92.49</td>\n",
       "      <td>43.50</td>\n",
       "      <td>53.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03 00:00:00+00:00</th>\n",
       "      <td>726.07</td>\n",
       "      <td>93.92</td>\n",
       "      <td>44.87</td>\n",
       "      <td>53.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04 00:00:00+00:00</th>\n",
       "      <td>676.00</td>\n",
       "      <td>94.02</td>\n",
       "      <td>41.32</td>\n",
       "      <td>53.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05 00:00:00+00:00</th>\n",
       "      <td>670.62</td>\n",
       "      <td>94.32</td>\n",
       "      <td>42.46</td>\n",
       "      <td>53.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              CMG    MCD   SHAK    WFM\n",
       "2015-01-30 00:00:00+00:00  709.58  92.42  45.80  52.10\n",
       "2015-02-02 00:00:00+00:00  712.55  92.49  43.50  53.15\n",
       "2015-02-03 00:00:00+00:00  726.07  93.92  44.87  53.41\n",
       "2015-02-04 00:00:00+00:00  676.00  94.02  41.32  53.67\n",
       "2015-02-05 00:00:00+00:00  670.62  94.32  42.46  53.38"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_prices = prices.dropna()\n",
    "dropped_prices.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Time Series Analysis with pandas\n",
    "\n",
    "Using the built-in statistics methods for `DataFrames`, we can perform calculations on multiple time series at once! The code to perform calculations on `DataFrames` here is almost exactly the same as the methods used for `Series` above, so don't worry about re-learning everything.\n",
    "\n",
    "The `plot()` method makes another appearance here, this time with a built-in legend that corresponds to the names of the columns that you are plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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jtWU35hYZHtegyjNCV3db7hzGVrE6+pUEQZfLGcBZqinM0dw1SrGEgwcP0qZN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\nYUcKISGEEEIIIcSw8/8B8pSp1zCgTkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01640b6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot()\n",
    "plt.title(\"Collected Stock Prices\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Date\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same statistical functions from our interactions with `Series` resurface here with the addition of the `axis` parameter. By specifying the `axis`, we tell pandas to calculate the desired function along either the rows (`axis=0`) or the columns (`axis=1`). We can easily calculate the mean of each columns like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMG     501.641213\n",
       "MCD      96.621592\n",
       "SHAK     53.532675\n",
       "WFM      45.592710\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as the standard deviation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMG     146.700133\n",
       "MCD       5.715712\n",
       "SHAK     11.951954\n",
       "WFM       7.772486\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the `describe()` function will provide us with summary statistics of our data if we would rather have all of our typical statistics in a convenient visual instead of calculating them individually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1006.000000</td>\n",
       "      <td>1006.000000</td>\n",
       "      <td>233.000000</td>\n",
       "      <td>1006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>501.641213</td>\n",
       "      <td>96.621592</td>\n",
       "      <td>53.532675</td>\n",
       "      <td>45.592710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>146.700133</td>\n",
       "      <td>5.715712</td>\n",
       "      <td>11.951954</td>\n",
       "      <td>7.772486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>236.240000</td>\n",
       "      <td>84.060000</td>\n",
       "      <td>38.205000</td>\n",
       "      <td>29.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>371.605000</td>\n",
       "      <td>93.675000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.792500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>521.130000</td>\n",
       "      <td>96.304950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>646.810000</td>\n",
       "      <td>99.135000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.727500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>757.770000</td>\n",
       "      <td>120.010000</td>\n",
       "      <td>92.470000</td>\n",
       "      <td>65.235000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               CMG          MCD        SHAK          WFM\n",
       "count  1006.000000  1006.000000  233.000000  1006.000000\n",
       "mean    501.641213    96.621592   53.532675    45.592710\n",
       "std     146.700133     5.715712   11.951954     7.772486\n",
       "min     236.240000    84.060000   38.205000    29.150000\n",
       "25%     371.605000    93.675000         NaN    39.792500\n",
       "50%     521.130000    96.304950         NaN    45.800000\n",
       "75%     646.810000    99.135000         NaN    51.727500\n",
       "max     757.770000   120.010000   92.470000    65.235000"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can scale and add scalars to our `DataFrame`, as you might suspect after dealing with `Series`. This again works element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03 00:00:00+00:00</th>\n",
       "      <td>631.960</td>\n",
       "      <td>147.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>647.480</td>\n",
       "      <td>148.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>649.980</td>\n",
       "      <td>149.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>647.900</td>\n",
       "      <td>151.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>629.045</td>\n",
       "      <td>149.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               CMG     MCD  SHAK    WFM\n",
       "2012-01-03 00:00:00+00:00  631.960  147.62   NaN  19.62\n",
       "2012-01-04 00:00:00+00:00  647.480  148.84   NaN  21.45\n",
       "2012-01-05 00:00:00+00:00  649.980  149.66   NaN  22.74\n",
       "2012-01-06 00:00:00+00:00  647.900  151.18   NaN  22.87\n",
       "2012-01-09 00:00:00+00:00  629.045  149.24   NaN  22.88"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2 * prices - 50).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the `pct_change()` method to get a `DataFrame` of the multiplicative returns of the securities that we are looking at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMG</th>\n",
       "      <th>MCD</th>\n",
       "      <th>SHAK</th>\n",
       "      <th>WFM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-04 00:00:00+00:00</th>\n",
       "      <td>0.022758</td>\n",
       "      <td>0.006173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05 00:00:00+00:00</th>\n",
       "      <td>0.003584</td>\n",
       "      <td>0.004124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06 00:00:00+00:00</th>\n",
       "      <td>-0.002972</td>\n",
       "      <td>0.007613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09 00:00:00+00:00</th>\n",
       "      <td>-0.027017</td>\n",
       "      <td>-0.009643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10 00:00:00+00:00</th>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.002881</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                CMG       MCD  SHAK       WFM\n",
       "2012-01-04 00:00:00+00:00  0.022758  0.006173   NaN  0.026286\n",
       "2012-01-05 00:00:00+00:00  0.003584  0.004124   NaN  0.018055\n",
       "2012-01-06 00:00:00+00:00 -0.002972  0.007613   NaN  0.001787\n",
       "2012-01-09 00:00:00+00:00 -0.027017 -0.009643   NaN  0.000137\n",
       "2012-01-10 00:00:00+00:00  0.003468  0.000402   NaN -0.002881"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult_returns = prices.pct_change()[1:]\n",
    "mult_returns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use our statistics methods to standardize the returns, a common procedure when examining data, then we can get a better idea of how they all move relative to each other on the same scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mhsFMPmEwMwp2HW6CPxgf7cMgIqJBenHpQSzfcma0D4OI0tiZGVVlmVk+YTCTY22BOH7w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\nmzuIRyRUy4vuKyF5Kmg4SLLEhMv3PuFmZfyqDweHsdxsdubU1HlsxyZ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td+9CbFpFisOfcoBgZjwzLPvDHIyrYwH761d/GXsM3tUumINpLRa7SNNRsaGcuKaqdf92GpBSrR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65jpzc3N0dHTYvn07KSkp/PGPfwToFuGWGhrbOjs76ezsZMuWLaSmpjJo0CCefvrpx6JNGhXT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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0164077f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_returns = (mult_returns - mult_returns.mean(axis=0))/mult_returns.std(axis=0)\n",
    "norm_returns.loc['2014-01-01':'2015-01-01'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes it easier to compare the motion of the different time series contained in our example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rolling means and standard deviations also work with `DataFrames`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rolling_mean = pd.rolling_mean(prices, 30)\n",
    "rolling_mean.columns = prices.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EtAigga+7nT4L2yo3W/hw7k7Kyi08PbIdnapxmtUDg1qy92gO63edZOOeTIb2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jJp7qMUNTeb3dzRftCvl0RohI59gzz/hw2jHrM7rLjcNkon5ZmjJq7hnqx0r1Z6kbjH6yDP75ww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\njlfXHKjimlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f015873b250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling_mean.plot()\n",
    "plt.title(\"Rolling Mean of Prices\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a complete list of all the methods that are built into `DataFrame`s, check out the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps\n",
    "\n",
    "Managing data gets a lot easier when you deal with pandas, though this has been a very general introduction. There are many more tools within the package which you may discover while trying to get your data to do precisely what you want. If you would rather read more on the additional capabilities of pandas, check out the [documentation](http://pandas.pydata.org/pandas-docs/stable/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "py35"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
